LCA Modelling for Zero Emission
Neighbourhoods in Early Stage Planning
Vilde Borgnes
Master of Energy and Environmental Engineering Supervisor: Helge Brattebø, EPT
Co-supervisor: Carine Lausselet, EPT Submission date: June 2018
Norwegian University of Science and Technology
Norwegian University Department of Energy
of Science and Technology and Process Engineering
EPT-M-2018-11
MASTER THESIS
for
Student Vilde Borgnes
Spring 2018
Scenario assessment in LCA for Zero Emission Neighbourhoods Scenariovurdering i LCA for nullutslippsområder
Background and objective
Future climate change mitigation targets will require large energy savings and greenhouse gas emission reductions in building stocks. One of the strategies as a response to these policies is the development of zero emission neighborhood (ZEN) concepts; for instance by urban development where the interplay of activities and subsystems at the neighborhood level give close to zero emissions. This MSc thesis work is on the development of life cycle assessment (LCA) models to support the evaluation of ZEN concepts with respect to greenhouse gas emissions and environmental impacts. Previous studies have mainly investigated the LCA characteristics and impacts of individual buildings. In parallel, much research has been done on energy-efficiency solutions for individual buildings. The Zero Emission Neighbourhood ini Smart Cities Research Centre (FME-ZEN) studies the energy and emission performance on a neighbourhood scale and investigates the combination of building-specific measures and local solutions on the neighbourhood scale. This thesis is related to the ongoing work at the ZEN Research Centre.
The overall objective of this thesis is to contribute to consistent use of LCA methods for ZEN concepts, with an appropriate structure of inventory datasets and a modelling framework for the evaluation of selected zero emission concepts with measures at different levels (temporal, spatial, organizational) in ZEN systems, with interacting subsystems such as building stock demand, inhabitants mobility needs, onsite energy generation, local energy storage and heat distribution, and import/export to external electricity or heat grids.
Previous research indicate that the following parameters may play an important role in such assessment: system boundaries, functional unit (absolute, spatial, per capita or multiple), inhabitants mobility, carbon intensity of the energy mix and building materials. The particular objective of this thesis is to examine how these different parameters influence LCA results (in particular climate change impacts) by developing scenarios, when modelling a ZEN system on a modular basis in a long-term perspective.
The work is linked to IndEcol’s participation in the FME-ZEN research center and PhD-student
The following tasks are to be considered:
1. Carry out a literature study relevant to the work of the project.
2. Develop a modular structure of a generic ZEN system, including buildings, mobility, infrastructure and energy supply and storage components, as a basis for an LCA inventory of the ZEN system. Then, use the modular structure to represent and specify more in detail a given case that may serve as an example of a ZEN project, such as Zero Village Bergen.
3. Develop an LCA model in Arda of the generic ZEN concept, or the specified case ZEN project. Collect data and information needed to populate the model with inputs to be run.
4. Develop scenarios based on development paths of chosen important factors/variables towards 2080.
5. Implement these scenarios in your LCA model and present results in order to document the case system performance under different scenarios. Discuss how the different core factors/variables influence the environmental performance of your system, with particular attention to the influence of system boundaries, on-site energy generation and storage, and the dynamics of emission intensity of electricity towards 2080.
6. Discuss strengths and weaknesses of your work, and suggestions for follow-up research.
-- ” --
Within 14 days of receiving the written text on the master thesis, the candidate shall submit a research plan for his project to the department.
When the thesis is evaluated, emphasis is put on processing of the results, and that they are presented in tabular and/or graphic form in a clear manner, and that they are analyzed carefully.
The thesis should be formulated as a research report with summary both in English and Norwegian, conclusion, literature references, table of contents etc. During the preparation of the text, the candidate should make an effort to produce a well-structured and easily readable report.
In order to ease the evaluation of the thesis, it is important that the cross-references are correct.
In the making of the report, strong emphasis should be placed on both a thorough discussion of the results and an orderly presentation.
The candidate is requested to initiate and keep close contact with his/her academic supervisor(s) throughout the working period. The candidate must follow the rules and regulations of NTNU as well as passive directions given by the Department of Energy and Process Engineering.
Risk assessment of the candidate's work shall be carried out according to the department's procedures. The risk assessment must be documented and included as part of the final report.
Events related to the candidate's work adversely affecting the health, safety or security, must be documented and included as part of the final report. If the documentation on risk assessment represents a large number of pages, the full version is to be submitted electronically to the supervisor and an excerpt is included in the report.
Pursuant to “Regulations concerning the supplementary provisions to the technology study program/Master of Science” at NTNU §20, the Department reserves the permission to utilize all the results and data for teaching and research purposes as well as in future publications.
The final report is to be submitted digitally in DAIM. An executive summary of the thesis including title, student’s name, supervisor's name, year, department name, and NTNU's logo and name, shall be submitted to the department as a separate pdf file. Based on an agreement with the supervisor, the final report and other material and documents may be given to the supervisor in digital format.
Work to be done in lab (Water power lab, Fluids engineering lab, Thermal engineering lab) Field work
Department of Energy and Process Engineering, 15. January 2018
Helge Brattebø Academic Supervisor
Co-supervisor: PhD student Carine Lausselet, IndEcol
Preface
The objective of this MSc thesis is to contribute to expedient use of life cycle assessment (LCA) of neighborhoods in an early planning stage, by focusing on contributors to
environmental impacts and critical factors. The work is linked to IndEcol’s participation in the FME-ZEN Research Centre and was carried out during the spring of 2018 at the Norwegian University of Science and Technology.
The scope and content are decided in consultation with the supervisor of the thesis and are to some extent deviating from the assignment text. This includes change of the title because of omission of development of scenarios (task 4 and 5). Instead, the model is based on a specified project (Zero Village Bergen) and in addition to a base case, a sensitivity assessment is performed to find critical parameters. Also, the model is not developed in Arda, but as a clean excel model (see task 3).
The thesis consists of (1) a research article with the title “Scenario assessment in LCA for Zero Emission Neighborhoods”, and (2) a supplement material describing the LCA model in detail to provide a broader understanding. The relevant parts of the supplement material are referred to in the article.
Thanks to my supervisor Helge Brattebø and co-supervisor Carine Lausselet for valuable help during the work.
The picture on the front page is illustrated by Snøhetta [1].
Abstract
The building sector is a major driver of climate change and recent years it has been a growing focus on limiting greenhouse gas (GHG) emissions associated with the built environment. In Norway, the Research Centre on Zero Emissions Neighborhoods (ZEN
Centre) has a goal of developing future buildings and neighborhoods with no GHG emissions.
To estimate the total emissions caused by buildings throughout the entire life cycle, life cycle assessment (LCA) is a commonly used and well-established tool. When studying more
complex systems as neighborhoods however, the existing research is scarce.
The objective of the work in hand is to contribute to expedient use of LCA of neighborhoods at an early planning stage, by focusing on contributors to environmental impacts and critical factors. An LCA model for ZENs based on a modular structure was developed with five included elements; buildings, mobility, open spaces, networks and on-site energy infrastructure. The model was tested on Zero Village Bergen, a pilot project for the ZEN Centre. The results give a total of 117 kg tonne CO2-eq over 60 years. The buildings
constitute the largest share of emissions among the elements with 52%, and the emissions embodied in the materials account for 56% when all elements are included. Critical
parameters are emission intensities for electricity and heat production by waste incineration, as well as the daily distance travelled by the inhabitants.
The model has clear potential to facilitate decision making in early stage planning of ZENs, as it provides information on dominant elements and life cycle stages, and its modular
structure ensures comparability and adaptability. On the other hand, the LCA model, and consequently also the results, suffer from uncertainties and simplifications, particularly on how technology and behavior may change in a long-term perspective. Further work is therefore suggested.
Sammendrag
Bygningssektoren er en betydelig bidragsyter til klimaendringene og de siste årene har det vært et stadig økende fokus på å begrense utslipp av drivhusgasser fra denne sektoren. I Norge har forskningssenteret for nullutslippsområder i smarte byer (FME ZEN) et mål om å utvikle fremtidens bygninger og nabolag uten klimagassutslipp. For å beregne utslippene fra bygninger gjennom hele livsløpet er LCA et anerkjent og godt etablert verktøy. Dersom vi utvider systemgrensene og ser på mer komplekse systemer som nabolag, er den tidligere forskningen noe mangelfull.
Målet med dette arbeidet er å bidra til hensiktsmessig bruk av LCA på nabolagnivå i tidligfase planlegging, gjennom å fokusere på bidragsytere til miljøpåvirkning og kritiske faktorer. Det er utviklet en LCA modell for nullutslippsområder basert på en modulær struktur bestående av fem elementer; bygninger, mobilitet, åpne plasser, nettverk og energiinfrastruktur.
Modellen er brukt på Zero Village Bergen, som er et av pilotprosjektene i FME ZEN med alle de fem elementene inkludert. Både produksjonsfasen, utskiftninger av materialer og
energibruk i drift inngår i analysen. Resultatene viser et utslipp av totalt 117 kg tonn CO2-eq over analyseperioden på 60 år. Blant elementene er det bygningene som står for den største delen av utslippene, med 52%. Når man ser på fasene i livsløpet til nabolaget, er det utslipp innbundet i materialer (produktfasen og utskiftninger) som utgjør hoveddelen av utslippene, med 56%. Kritiske parametere er utslippsintensiteter for elektrisitet og avfallsforbrenning i tillegg til den daglige reiseavstanden til innbyggerne.
Den utviklede modellen har et klart potensiale for å legge til rette for tidligfase planlegging for nullutslippsområder, og den gir verdifull informasjon om betydelige bidragsytere og livssyklusfaser. I den modulære strukturen er det mulig å justere systemgrenser og
funksjonell enhet for å muliggjøre sammenligning av ulike prosjekter. På den annen side er modellen, og dermed også resultatene, preget av usikkerhetsmomenter og forenklinger, spesielt når det kommer til hvordan teknologi og brukeratferd vil endre seg i et
langtidsperspektiv. Videre arbeider er derfor foreslått.
TABLE OF CONTENTS
Article: LCA Modelling for Zero Emission Neighbourhoods in Early Stage Planning ... 3
1. Introduction ... 4
1.1. Environmental Assessment of Buildings ... 4
1.2. From Buildings to Neighbourhoods ... 5
1.3. Problem Statement ... 7
2. Material and Methods ... 8
2.1. Modular Structure ... 8
2.2. LCA Model for Zero Village Bergen ... 10
2.3. Sensitivity Analysis ... 17
3. Results ... 18
3.1. General Results ... 18
3.2. Results Sensitivity Analysis ... 20
4. Discussion ... 21
4.1. LCA Modelling on Neighbourhood Scale – Results and Critical Parameters ... 21
4.2. Limitations and Further Work ... 23
5. Conclusion ... 25
Supplement Material ... 27
S1. The Life Cycle Stages of The Building (From prNS 3720)... 28
S2. Emission Intensities ... 29
S2.1 Electricity ... 29
S2.2 District Heat ... 30
S3. Modular Structure Zero Village Bergen ... 32
S4. Map ... 33
S5. Buildings ... 34
S5.1 Number of Inhabitants ... 34
S5.2 Area Inside Parking... 35
S5.3 Materials Buildings ... 36
S5.4 Energy Use in Operation of Buildings ... 42
S6. Mobility ... 43
S6.1 Travel Distances by Transport Mode ... 43
S6.2 Evolution of Vehicle Stocks ... 44
S6.4 Energy Use and Emissions in Operation (B6) (2010 values)... 46
S6.5 Future Emissions from Operation ... 47
S7. Open Spaces ... 48
S7.1 Dimensions of the Road ... 48
S7.2 Materials included in the Open Spaces ... 49
S7.3 Number of Hours with Need for Public Lighting ZVB ... 50
S8. Networks ... 51
S8.1 District Heating Network in Bergen... 51
S8.2 Materials included in the District Heating Network ... 52
S9. On-site Energy ... 53
S10. Results ... 54
S10.1 Total Emissions by Element and Life Cycle Stage ... 54
S10.2 Mobility – Emissions associated with Replacements ... 55
S10.3 Mobility – Operation ... 56
S10.4 Result Details Buildings ... 57
References ... 58
Article: LCA Modelling for Zero Emission Neighbourhoods in Early Stage Planning
Author: Vilde Borgnes
Keywords: Life Cycle Assessment (LCA), Zero Emission Neighbourhoods, Early stage planning
Abstract
The building sector is a major driver of climate change and recent years it has been a growing focus on limiting greenhouse gas (GHG) emissions associated with the built environment. In Norway, the Research Centre on Zero Emissions Neighbourhoods (ZEN Centre) has a goal of developing future buildings and neighbourhoods with no GHG emissions. To estimate the total emissions caused by buildings throughout the entire life cycle, life cycle assessment (LCA) is a commonly used and well-established tool. When
studying more complex systems as neighbourhoods however, the existing research is scarce.
The objective of the work in hand is to contribute to expedient use of LCA of
neighbourhoods at an early planning stage, by focusing on contributors to environmental impacts and critical factors. An LCA model for ZENs based on a modular structure was developed with five included elements; buildings, mobility, open spaces, networks and on- site energy infrastructure. The model was tested on Zero Village Bergen, a pilot project for the ZEN Centre, with product stage, replacements and energy use in operation as included life cycle stages for all the elements. The results give a total of 117 kg tonne CO2-eq over 60 years. The buildings constitute the largest share of emissions among the elements with 52%, and the emissions embodied in the materials account for 56% when all elements are
included. Critical parameters are emission intensities for electricity and heat production by waste incineration, as well as the daily distance travelled by the inhabitants.
Graphical Abstract
-5000 5000 15000 25000 35000 45000 55000 65000
Buildings Mobility Open spaces Networks On-site energy
tonne CO2-eq over lifetime
Total emissions by element and life cycle stage
Product stage A1-A3 Replacements (B4) Energy use in operation (B6)
1. Introduction
The 2015 Paris agreement of an average global temperature rise of maximum 2 degrees compared with pre-industrial times [2] has led to a growing focus on climate change. The building sector is a major driver, accounting for about one third of both energy consumption and greenhouse gas (GHG) emissions globally (2010) [3]. With the aim of reducing the energy use in buildings through country-level regulation, the EU has established two legislative directives; the Energy Performance of Buildings Directive (EPBD) [4] and the Energy Efficiency Directive [5]. This has motivated research, creation of building codes and development of concepts, which add guidance for energy efficiency in buildings. In Norway, the Norwegian Research Centre on Zero Emission Buildings (ZEB Centre) was a research project running from 2009 to 2017, with a vision to eliminate the GHG emissions caused by buildings. Its main objective was to develop competitive products and solutions for existing and new buildings leading to market penetration of buildings that have zero emission of GHGs related to their production, operation and demolition [6]. A Research Centre on Zero Emission Neighbourhoods in Smart Cities (ZEN Centre) is recently started as a follow-up of the ZEB Centre, with a goal to develop solutions for future buildings and neighbourhoods with no GHG emissions and thereby contribute to a low carbon society [7]. With this expansion in scope, the ZEN Centre researchers already acknowledge that many additional questions and challenges arise, and it is less obvious what good choices are and how to use LCA for decision support, e.g. regarding functional unit(s), system boundaries and assumed input values for critical variables and parameters.
1.1. Environmental Assessment of Buildings
To implement efficient reductions of environmental impacts from buildings, knowledge on the impacts over the entire life span of the building is essential. For this purpose, life cycle assessment (LCA) is a common and well-established tool [8-10]. LCA systematically addresses the environmental impacts of a system through the life cycle stages, from raw material acquisition, through energy and material production, to use and end-of-life treatment [11].
LCA studies at building level have led to valuable results that are used to pave the way for emission reductions in the building sector [12, 13].
One important finding is how the relative importance of the emissions from the operation of the individual building (heating, cooling, lighting, ventilation and appliances) compared to the emissions embodied in the materials used in the building have changed over time as a consequence of improved technology and building codes. Historically, the results have shown that the use stage is dominating, accounting for 80-90% of the total emissions [13- 15]. More recent studies however, concluded that especially when low-energy buildings are evaluated, the share of the emissions from the materials are considerable [16-20]. Wiik et al.
[19] found that the embodied emissions (including the production stage of materials and
replacements during the life cycle of the building) accounted for as much as 55-87% of the total GHG emissions for Norwegian ZEB case studies examined by the ZEB Centre.
When focusing on the other stages of the life cycle, previous research indicates that 2-15%
of the emissions are driven by the construction stage [19, 21, 22]. Yang et al. [21] however, found that among all the life cycle stages, the construction and the demolition stages together represented less than 1% of the total carbon emissions for a residential building in China.
Other lessons-learnt from LCA on buildings are related to e.g. alternative and renewable materials, architectural design (as shape, envelope and passive heating and cooling systems), user behaviour, and energy-positive buildings and the associating consequences of a greater exchange of self-produced energy to external grids [23-27]. Findings here may be just as relevant when focusing on more complex systems as neighbourhoods, it is nevertheless chosen not to go into detail about these topics here.
1.2. From Buildings to Neighbourhoods
In recent years it has been a focus shift when performing environmental assessments – from concentrating on individual buildings, treated as objects independent of the surrounding environment, to consider stocks of buildings and larger systems, as cities or neighbourhoods [28-30].
Still, the LCA literature on neighbourhood level is scarce and highly reasoned by the complexity and context dependency of the systems studied, the LCAs are characterized by heterogeneous approaches [20, 29].
The choice of system boundaries is a factor that excels from previous research, and the boundaries are shown to have considerable impacts on the results. The boundaries define what to include in the analysis, both regarding life cycle stages and physical elements such as buildings, mobility, open spaces and infrastructure. Some research is concentrated on
clusters of buildings [31, 32], other take into consideration also the users´ mobility [30, 33- 35]. The most complex LCA studies include both buildings, mobility and other elements as open spaces and networks [24, 36, 37]. The life cycle stages considered also vary, from only looking at the use stage, to consider also the construction and deconstruction stages [20, 29]. The different choices of system boundaries lead to difficulties when it comes to
comparing results from LCA studies. Nevertheless, some important take-away messages are worth noting.
When focusing on the physical elements, the daily mobility of inhabitants seems to have a considerable impact on total emissions. Bastos et al. [35] found that user transportation contributed to 51-57% of the total GHG emissions when materials included in construction of the buildings, the use stage, and transportation were included in the analysis. Also Nichols and Kockelman [24] found that transportation constituted a considerable share of the
also the manufacturing of the modes of transport, but there are exceptions; Stephan et al.
[36] found that indirect emissions, (including among other things vehicle manufacturing and building roads) constituted 52% of the total emissions from transportation. Anderson et al.
[30] found the same number to be 22-27%, depending on the location of the neighbourhood (city centre, periphery or district). The large contributions and difference in results from these studies indicate that much more research is required on the field of indirect impacts from mobility related to zero emission neighbourhoods. Fortunately, these issues are already on their way into standards, such as the proposed new Norwegian standard prNS 3720 Method for greenhouse gas calculations for buildings [38], which expands the system boundaries compared to the European standard EN 15978:2011 Sustainability of
construction works – Assessment of environmental performance of buildings – Calculation method [39], by including transport in the use stage as a new module in calculations of GHG emissions from buildings, see S1 in supplement material.
Temporal aspects and assumptions about the future are crucial when performing LCA, and the long lifespan of elements in neighbourhoods makes the forecasting of emissions difficult and a subject to uncertainty. This is highlighted in several studies, and especially the
emission intensity of electricity (g CO2-eq/kWh), evolving technology and time distribution of environmental impacts are considered key factors [29, 30, 35, 36, 40]. These factors may have considerable impact on long-term decisions and prediction of future emissions and should therefore be investigated further.
Furthermore, a common feature for the existing research is that the studies are usually conducted on existing neighbourhoods, cities or districts. However, the power of LCA is only fully utilized when it is also used as a tool in early stage planning of new neighbourhood projects. Lotteau et al. [41] describe a tool called NEST (Neighbourhood Evaluation for Sustainable Territories), an LCA tool for assessment of environmental impact of urban projects, developed by Yepez-Salmon [42]. By including the production stage, maintenance, use and end-of-life for both buildings and open spaces, as well as the daily mobility of the inhabitants, the tool makes it possible to look at different solutions for neighbourhood projects. The tool has been used in urban planning projects in France, and a holistic approach like this should be explored also in neighbourhood projects elsewhere.
More research is obviously required in the field of LCA on Zero Emissions Neighbourhoods.
This regard both what life cycle stages and physical elements in the neighbourhood that contribute significantly to different categories of environmental impact, and wider knowledge of critical factors that affect the results under varying context situations. Such knowledge is fundamental and should serve as a foundation for the development of ZEN concepts.
1.3. Problem Statement
The objective of the work in hand is to contribute to expedient use of LCA of
neighbourhoods at an early planning stage, by focusing on contributors to environmental impacts and critical factors. Through development of a model tested for a ZEN project in the early planning stage located in Bergen, Norway, the following research questions are
answered:
• What are the dominant physical elements and life cycle stages contributing to the total environmental impact on a neighbourhood scale?
• What are the critical factors that affect these contributions and what are their sensitivity?
• What are the strength and weaknesses of the model that is developed? Can it provide useful inputs to the early stage planning process of a Zero Emission Neighbourhood?
2. Material and Methods
The work in hand consists of a suggestion of an expedient modular structure that works as a basis for LCA on neighbourhood level as well as the development of an LCA model for a specific neighbourhood using this structure. The specific case study is based on a pilot project for the ZEN Centre, called Zero Village Bergen (ZVB), located in Norway. The project is in the planning stage with presumed commencement in some years, and it is going to be Norway’s biggest zero emission project for buildings [1]. Although the model is adapted to the specific case, the methodology and calculation procedures are intended to also be applicable to other LCA projects at neighbourhood level.
2.1. Modular Structure
The modular structure suggested is presented in Figure 1 and consists of two dimensions to cover both the physical elements (buildings, mobility, open spaces, networks and on-site energy infrastructure), and the life cycle stage modules included in the LCA. The latter is described by ambition levels, and the different modules (A1-C4) are based on the suggestions in prNS 3720 [38]. Because mobility is included as a separate element, the transportation in use (B8) is considered irrelevant (marked with grey in the figure).
The ambition levels are based on the approach used by the ZEB Centre and describe the life cycle stages included for each of the physical elements. The following description of these is adapted from the ZEB definition [43].
• ZEN O: Emissions related to all operational energy "O".
• ZEN OM: Emissions related to all operational energy "O" plus embodied emissions from materials "M.”.
• ZEN COM: The same as OM, but also considers emissions relating to the construction "C"
stage.
• ZEN COME: The same as ZEB-COM, but also considers emissions relating to the end of life
“E” stage.
The elements and ambition levels (and associated life cycle stages) can be adjusted to match the neighbourhood of interest for each assessment.
Figure 1 Modular structure used as basis for LCA at neighbourhood level. Note: the elements and ambition levels are randomly selected and serve as an example of the use of the structure.
At the top left side of the structure, the emission intensity for electricity is stated (here it is chosen to be “Norwegian”). In Norway, the coming standard on method for greenhouse gas calculations in buildings [38] suggests to look at two different scenarios for the emission intensity of electricity, scenario 1 (NO) and scenario 2 (EU28+NO) based on the Norwegian and the European production mix, respectively. In practice, scenario 1 considers Norway as an isolated electricity system without import/export of electricity, and scenario 2 assumes that electricity is flowing freely between European countries, including Norway. Details on the emission intensities are given in S2.1 and Figure 2 represents the two scenarios with evolution from 2015 to 2080.
Figure 2 Evolution of emission intensities for electricity (g CO2-eq/kWh) 2015-2080 based on scenarios suggested in prNS 3720 [38].
0 50 100 150 200 250 300 350 400
2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 g CO2-eq/kWh
Emission intensities electricity
Scenario 1 (NO) Scenario 2 (EU28+NO)
2.2. LCA Model for Zero Village Bergen
An LCA model was developed for Zero Village Bergen (ZVB) using the modular structure presented in Section 2.1. For all the elements (buildings, mobility, open spaces, networks and on-site energy infrastructure) ambition level “ZEN-OM” was applied, including the production stage (A1-A3), as well as replacements (B4) and energy use in operation (B6). An exception is for the networks, where the energy use in operation is excluded due to an assumed low impact. The modular structure adapted to the present study, as well as a map of the neighbourhood is presented in S3 and S4 respectively. The analysis period, equivalent with the assumed lifetime of buildings and infrastructure, is 60 years and it is focused on GHG emissions associated with each of the elements throughout this period. At the planning stage in the project, different energy system alternatives are under consideration, including joining the district heating system already present in Bergen, a local CHP plant or ground source heat pumps [44]. In the present study it is assumed that the heat demand is covered by connecting to the district heating system in Bergen, and that the electricity demand is supplied from the external power grid and with local production of electricity by
photovoltaic panels. Regarding the emission intensity, scenario 1 (NO) is chosen for both import and export of electricity between the neighbourhood and the external power grid.
2.2.1. Buildings
The building stock in ZVB consists of residential buildings and non-residential buildings, with a total area of 91 891 m2 [45], see Table 1. The total number of dwellings is 695 and based on statistics these are home to 1 340 inhabitants, see S5.1. The underground parking garages are not included in the total floor area of ZVB, but the embodied emissions in their materials are included in the product stage and replacements. The area of parking is estimated based on information of number of parking spots [45], see S5.2.
Table 1 Building stock and areas in ZVB [45].
Building type Floor area (m2)
Terraced house 62 136
Apartment block 23 028
Total residential 85 164
Kindergarten 1 061
Office 2 833
Shop 2 833
Underground parking 21 657
Total non-residential (excl. parking) 6 727
Total ZVB (excl. parking) 91 891
Production and replacement stages
The emissions embodied in building materials, Eb,mat, come from the initial materials
contained in the buildings, as well as replacements of materials each year throughout the 60 years period, see Equation 1.
Equation 1 Emission from building materials (products stage and replacements)
!",$%& = ( )*+!$%&,,-,& /"&∗ 1"&2 + ( *+!$%&,4567/
,,"& ∗ 1"&2
89
,:9
;
"&
Emat,init represents the emissions embodied in the materials initially contained in the buildings (CO2-eq/m2), Emat,repl denotes the emissions embodied in the materials used in replacements (CO2-eq/m2), bt is the building type, A the area (m2 floor area) and i is the year.
Material lists are presented in S5.3. Because of a limited access to detailed data, and
uncertainties in design choices at the early stage planning, all the residential buildings (both apartment blocks and terraced houses) were assumed consisting of the same amount of materials per area. The same goes for the non-residential buildings (all the non-residential buildings considered are equal in materials as the office building). For residential buildings and parking garages the material lists were provided by SINTEF (operator of the ZEN Centre), and for non-residential buildings the material list was based on the materials included in a pilot project for an office building performed by the ZEB Centre [46]. For both building types, the emission of GHGs per amount of material was based on either EPDs or the Ecoinvent database. The replacements are based on estimated service life of each material, and the emissions embodied in the replacement materials (B4) are assumed equal to the ones in the initial product stage (A1-A3).
Energy use in operation
The energy use in the buildings is based on work performed by the ZEB Centre [45] where the buildings in ZVB were simulated, giving a total thermal load of 3 283 MWh and a total electric load of 3 257 MWh per year, see S5.4. Figure 3 shows the yearly load in kWh/m2 for the different building types.
Figure 3 Yearly load (in kWh/m2) (adopted from [45])
It is assumed that the loads are constant for all the years in the analysis period. While the electric load is covered by electricity, the thermal demand (for space heating and domestic hot water) is covered by connecting to the district heating network in Bergen. The intensity of the district heat is calculated based on the emission intensities for the specific sources of energy. In Bergen, 87% of the energy comes from waste incineration and the emission intensity of the district heat is assumed to be 163.2 g CO2-eq/kWh in 2020 when emissions from waste incineration are allocated to the district heating production, see S2.2.
2.2.2. Mobility
Three means of transport are considered for the mobility in ZVB; personal vehicle, bus and light rail. Due to the extensive planning for public transport and cycling facilities [1], the distance travelled with each type is based on statistics on travel habits for people with very good access to public transport, see S6.1.
Although the new Norwegian standard prNS 3720 suggests including transportation of users, it does not include a methodology for calculating the emissions for different means of
transport. Nevertheless, it is suggested to use a project performed by the Norwegian
research institute Vestlandsforskning, completed in 2011, as a source for indicative emission factors for todays´ situation [38]. The documentation behind the results reveals large
heterogeneity when it comes to data on energy use and emissions from different means of transport from previous research [47], but concludes with providing chosen estimates for several transportation modes intended for Norwegian conditions.
Future evolution of the fuel types/energy carriers, together with technical improvements for vehicles and fuel chains make the forecast of emissions from transport a complex task. In prNS 3720, it is emphasized that development and technical improvements influenced by regulation and tax systems will lead to reduced emissions per distance driven during the buildings´ life cycle, and that this should be taken into account through scenario assessment [38]. In the work in hand, numbers from Vestlandsforskning is used as a basis for 2010, and
0 20 40 60 80 100 120 140
Terraced house Apartment block Non-residential
kWh/m2/year
Yearly Load
Thermal (sum) Electric (sum) Thermal: Heating Thermal: DHW
Electric: Fans and pumps Electric: Lighting Electric: Plug loads
several studies from the literature are used to predict the evolution in time (for both fuel types and technological improvements) up to 2080.
Evolution of vehicle stocks
The evolution of vehicle stocks is based on a “ultra-low emission policy scenario” developed by Fridstrøm and Østli [48]. The scenario is based on targets compiled by the transportation agencies, and the evolution of passenger cars and buses distributed between fuel
types/energy carriers is forecasted from 2010 to 2050. In the present study, the situation is simplified to only consider four types of fuel/energy carriers; battery, hydrogen, diesel and gasoline, and the trend is assumed to continue up to 2080 (see Figure 4). It is assumed that the light rail is all-electric throughout the entire period.
Figure 4 Evolution of vehicle stock for a) passenger cars and b) buses by fuel type/energy carrier used in present study (See data in S6.2)
Product and replacement stages
The emissions embodied in the materials for the mobility, Em,mat, were calculated using Equation 2.
Equation 2 Emission from materials in mobility (products stage and replacements)
!$,$%& = ( (<(!$%&)&$ ∗ ?&@&,&$,,A
&$
89
,:9
Emat denotes the emissions from the production of different vehicle types (CO2-eq/km) and Ltot describes the total neighbourhood yearly travel length (km). Tm is the transport mode (e.g. personal vehicle diesel), and i is the year.
The emissions from the product and replacement stages of the transportation are based on the project performed by Simonsen [47]. Because of the continuous replacements of vehicles, the emissions are considered per distance driven (see S6.3), and it is not distinguished between the initial material inputs (A1 – A3) and replacements (B4).
0%
20%
40%
60%
80%
100%
2010 2020 2030 2040 2050 2060 2070 2080
a)
2010 2020 2030 2040 2050 2060 2070 2080
b)
Diesel Gasoline Battery Hydrogen
The emissions embodied in the vehicles per distance are assumed constant throughout the 60 years period, but the total emissions from production of vehicles change due to the evolution of fuel/energy carrier types as described in Figure 4.
Energy use in operation
When it comes to the operation of the mobility it is distinguished between the vehicle cycle and the fuel cycle. Tank-to-wheel is used to describe the energy the vehicle uses for the actual propulsion (used regardless of the fact that the vehicle has actual wheels). Well-to- tank is used to describe the energy that is required to transform the energy source to a useful energy carrier as well as transport of the energy carrier to the user. Finally, well-to- wheel describes the summation of the two.
The total emissions from the operation of mobility, Em,oper, is calculated using Equation 3.
Equation 3 Total neighbourhood emissions from mobility operation
!$,@654 = ( ( ?&@&,&$ ∗
&$
BCB&$,,
89
,:D
Here, Ltot,tm is again the total neighbourhood yearly travel length (km/y), tm stands for transport mode and i is the year. WtWtm,i therefore denotes the emissions per km driven by transport mode tm in year i (kg CO2-eq/km).
The results from the project performed by Simonsen [47] were used as a starting point in 2010, see S6.4. Improvements in the fuel intensities were based on a study performed by Ajanovic [49], where scenarios for fuel intensities of new passenger cars were forecasted up to 2050, see S6.5. The formula used to calculate the WtW emissions from each of the
transport modes, tm, a given year, i, is represented in Equation 4.
Equation 4 Well-to-wheel (WtW) emissions
BCB&$,, = (!EFGHIJ&K,, ∗ LJ&K) + (!EFGHIJ&K,, ∗ LK&J)
In the equation, EnergyTtW denotes the propulsion energy needed (MJ/vkm), ITtW is the direct emission intensity (g CO2-eq/MJ) and IWtT is the emission intensity for the fuel cycle of the fuel/energy carrier (g CO2-eq/MJ). The latter are emissions associated with producing and transporting the fuel needed for the given energy in the propulsion of the vehicle.
As Equation 4 indicates, the intensities (both tank-to-wheel and well-to-tank) are held
constant, while the propulsion energy is assumed to change during the years. Figure 5 shows the evolution in the WtW emissions in g CO2-eq/passenger-km for the relevant modes of transport in snapshots for 2020, 2040, 2060 and 2080.
Figure 5 Evolution of WtW emissions from different modes of transport (see Table 15 in S6.5)
2.2.3. Open Spaces
Included in the open spaces element are emissions embodied in roads (included bicycle lanes), sidewalks and outside parking, as well as emissions from the operation of public lighting.
Product and replacement stage
It is assumed that the road network in ZVB consists of two types of road; (1) wide road with two lanes and bicycle lanes at each side and (2) narrow road without bicycle lanes. The road structure (material and dimension) is adopted from the work performed by Birgisdóttir et al.
[50], see S7.1. The area of each of the sub-elements are roughly estimated based on the map of ZVB (S4), see Table 2.
Table 2 Open spaces ZVB
Open spaces element Length (m) Area (m2)
Road type 1 3 700 63 640
Road type 2 4 400 49 280
Sidewalk 3 700 11 100
Parking - 2 900
The emissions from the materials in the open spaces elements are based on data from EPDs.
It is assumed lifetimes of 20 and 40 years for the surface asphalt and base asphalt courses respectively and 60 years for the aggregates. S7.2 shows the materials included in the open spaces elements.
0 20 40 60 80 100 120 140
Gasoline Diesel Electric Hydrogen Diesel Electric Hydrogen Electric
Personal vehicle Bus Light Rail
g CO2-eq/pkm
Evolution of WtW emissions
2020 2040 2060 2080
Energy use in operation
The emissions from the public lighting in ZVB, Eo,oper, are calculated using Equation 5.
Equation 5 Total neighbourhood emissions from operation of open spaces (public lighting)
!@,@654 = ( M ∗ N ∗ ℎ ∗ L57,,
89
,:9
N is the number of lighting units, P is the power per unit (kW) and h denotes hours with lighting per year. The number of hours the units are turned on is calculated using specific data for Bergen, see S7.3. Iel is the emission intensity for electricity and i represents the year.
2.2.4. Networks
For all the alternative energy system solutions in ZVB (district heat, local CHP or ground source heat pump), a local thermal network will connect the buildings with the energy central [44]. In the present study, this is the district heating network that connects ZVB to the already existing network in Bergen, see S8.1. The emissions embodied in the materials included in the part of this network geographically located inside the neighbourhood is therefore considered, with components at the neighbourhood system level (not on building or dwelling level). The energy use in operation of the network is not included.
Production and replacement stages
The length of pipes and number of units of the components are roughly estimated based on the design of ZVB, resulting in 5 000 m of new pipes (including both flow and return pipes) and one new pump. The amount of materials included is adopted from the study by Oliver- Solà et al. [51], where LCA was performed on a 100 m district heating system delivering energy to 240 dwellings by both including the neighbourhood-, building- and dwelling systems. The average diameter of the pipelines (100 mm) is from the study. The resulting material list and estimated service life for the pipes and the pump are presented in S8.2.
2.2.5. On-site Energy
The on-site energy in ZVB consists of photovoltaic panels placed on the building roofs. The dimensions and the generation of electricity used in the calculations are according to the report by Sartori et al. [45].
Production and replacements
The panels are placed on available roof area at the buildings, and the total PV area is 22 045 m2 [45]. Emissions associated with the production of the panels are found using Ecoinvent, see S9.1. The lifetime of the photovoltaic panels is assumed to be 30 years [52], and based on a suggestion from the ZEB Centre, a reduction of 50% of environmental impacts
compared to the initial production due to technology development and efficiency improvements is applied in the replacement [43].
Energy use in operation
Based on available roof area, meteorological data, system efficiency and losses, and generation profiles, the yearly PV generation is estimated to 2 941 MWh [45]. Emissions associated with this generation are calculated using the emissions intensity for electricity (scenario 1), and these emissions are seen as negative contributions to (i.e. avoided) emission because the electricity production from the PVs is a contribution to the electricity demand. It is either self-consumed in the neighbourhood or exported to the external electricity network.
2.3. Sensitivity Analysis
With the goal of investigating the critical parameters in the LCA model, a sensitivity analysis was performed on selected factors that were expected to have considerable impacts on the results and/or were associated with large uncertainties. All of the selected factors were increased with 25%, and the sensitivity ratio (SR) was measured using Equation 6.
Equation 6 Sensitivity ratio method
PQ =∆Q Q⁄ 9
∆N N⁄ 9
∆P/P0 represents the relative change in the input parameter and ∆R/R0 denotes the relative change in results.
In addition to this, two different assumptions expected to have a great impact on the results were examined, namely the emission intensity for electricity and the allocation of emissions associated with waste incineration at the district heating energy central. For the latter, the emission intensity for district heat was estimated to 16.1 g CO2-eq/kWh assuming
significantly less emissions from the heat generated by waste incineration (compared to 163.2 g CO2-eq/kWh used in base case), see S2.2.
3. Results
3.1. General Results
With the methodology described, the total emissions associated with the physical elements (buildings, mobility, open spaces, networks and on-site energy) and the life cycle stages (A1- A3, B4 and B6) were calculated, resulting in a total of approximately 117 kg tonne CO2-eq over the lifetime of 60 years. This equals 1.5 tonne CO2-eq/capita/year and 21.2 kg CO2- eq/m2/year (heated building area). The emissions are distributed between the elements and life cycle stages as shown in Figure 6. As indicated in the figure, the building element stands for the majority of the emissions, accounting for about 52% of the total emissions over the lifetime. The mobility is the second most contributing element, responsible for 40% of the total emissions. The emissions from the networks and open spaces constitute only 2.3%
together. Further, it is worth noticing the relatively small negative emissions from the on-site energy which, with the assumptions made, are actually less than the emissions associated with the production of the photovoltaic panels.
Figure 6 Total emissions for ZVB distributed between elements and life cycle stages (see S10.1 for data)
The results show that the emissions from the product stage (pre-use, A1-A3) represent a significant share (24%) of the total emissions when all elements are considered. This is without the production stage of vehicles in the mobility element (recall that this is merged with the replacement stage due to the shorter service life of vehicles). If we disregard these emissions and focus on the emissions occurring in the use stage, the emissions are
distributed over the years as presented in Figure 7. Emissions embodied in materials used in replacements for buildings, open spaces, networks and on-site energy (PV panels) are represented with emission peaks at certain points in time, while the emissions associated
-5000 5000 15000 25000 35000 45000 55000 65000
Buildings Mobility Open spaces Networks On-site energy
tonne CO2-eq over lifetime
Total emissions by element and life cycle stage
Product stage A1-A3 Replacements (B4) Energy use in operation (B6)
(light green bars). These emissions are slowly increasing due to the shift from fossil fuel vehicles to battery electrical and hydrogen electrical vehicles.
Figure 7 Total use stage emissions by year distributed by element and life cycle stage
To take a closer look at the parameters leading to the overall emissions, the two elements that stand for the major part of the emissions, buildings and mobility, are reported in detail.
For the mobility element, replacement of vehicles is the major emission source and
production of personal vehicles stand for as much as 96% of these emissions, see S10.1 and S10.2. While these emissions increase over the lifetime due to the increased share of battery electric vehicles, the emissions associated with the operation of the mobility decrease drastically for the same reason. When the total period of 60 years is considered, the internal combustion engine vehicles (both personal vehicles and buses) are dominating with 89% of the emissions, this despite the fact that these vehicles are assumed being completely phased out by 2060, see S10.3.
When focusing on the buildings, it is revealed that energy use in operation accounts for the majority of the emissions with 59%. Out of this, 91% is from district heat for space heating and domestic hot water. Regarding the materials, residential buildings obviously account for most of the emissions (the neighbourhood consists of 93% residential buildings), but this is amplified by the fact that also when looking at emissions per area, the residential buildings stand for relatively more emissions, see S10.4.
-100 900 1900 2900 3900 4900 5900 6900
2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 2052 2054 2056 2058 2060 2062 2064 2066 2068 2070 2072 2074 2076 2078 2080 tonne CO2-eq
Use stage emissions
Buildings Operation Mobility Materials Mobility Operation
Open Spaces Materials Open Spaces Operation Networks Materials
On-site Energy Materials On-site Energy Operation Buildings Materials
3.2. Results Sensitivity Analysis
The results of the sensitivity analysis are represented in Table 3, and reveal that the two parameters with the largest sensitivity ratio, and therefore the largest influence on change in total emissions results, are the travel distance per inhabitant and the buildings’ energy loads.
Table 3 Results sensitivity analysis selected parameters
Sensitivity parameter Sensitivity ratio Change in total emissions
result from base case
Emission intensity electricity +25% 0.021 0.5%
Emission intensity district heat +25% 0.279 7.0%
Travel distance/inhabitant/year +25% 0.403 10.1%
Emissions associated with vehicle production +25% 0.252 6.3%
Emissions embodied in building materials +25% 0.165 4.1%
Energy load (thermal and electric) +25% 0.306 7.7%
Area of PV panels +25% 0.055 1.4%
Energy public lightng +25% 0.005 0.1%
Figure 8 shows the change relatively to the base case for each of the parameters and also for two fundamental assumptions that are shown to have a considerable impact on the results, namely the emission intensity for the electricity and the assumption of allocating the emissions associated with the waste incineration to the waste management system rather than to the district heating production. If scenario 2 (see section 2.1) is used, the total emissions over the 60 years analysis period of the neighborhood will increase with 12.5%.
This is despite that also the negative emissions from the on-site electricity production will be larger. If the emissions from waste incineration is not allocated to the district heating
production, the total emissions are decreased with 25.3%.
Figure 8 Results sensitivity analysis relatively to the base case. Notice that the axis does not start at zero.
80 90 100 110 120 130 140
CO2 from waste not allocated to energy production Emission intensity electricity as Scenario 2 Emission intensity electricity +25%
Emission intensity district heat +25%
Travel distance/inhabitant/year +25%
Emissions associated with vehicle production +25%
Emissions embodied in building materials +25%
Energy load (thermal and electric) +25%
Area of PV panels +25%
Energy public lightng +25%
Base case
kgtonne CO2-eq over lifetime
4. Discussion
In this section the modular structure presented in section 2.1 and the model developed for Zero Village Bergen described in section 2.2 are discussed. The results obtained from the model (section 3) are discussed in the context of the research questions presented in section 1.3, and critical factors and uncertainties are deliberated. Finally, usefulness and limitations are discussed, and further work required on the field of LCA modelling for Zero Emission Neighbourhoods is suggested.
4.1. LCA Modelling on Neighbourhood Scale – Results and Critical Parameters When moving from individual buildings to complex systems as neighbourhoods in LCA modelling, it is crucial to clearly understand the effect of preconditions made, and elements and life cycle stages included. With the modular approach, it is possible to look at the effect of changing the system boundaries, both regarding elements and life cycle stages included, and also to present the results with several functional units. The modules make it possible to easily adjust the LCA to fit different neighbourhood projects (with different preconditions) and to compare different projects with different premises.
The model developed for Zero Village Bergen based on the modular structure led to results that provide useful insight in the dominant physical elements and lifecycle stages
contributing to environmental impact. It revealed that buildings account for as much as 52%
of the total emissions (with a ZEN OM ambition level for all elements). When looking at the buildings alone, the emissions embodied in the materials stand for 41% of the total
emissions (for the three stages considered). This is comparable to, but not quite as much as reported by Wiik et al. [19], who stated that the share was between 55% and 87%. It should be noted that the emissions embodied in materials in the present study may be
underestimated because of uncompleted material lists for the residential buildings. Another important aspect is the fact that out of the remaining 59% of the emissions caused by energy use, as much as 91% is associated with heat supply for space heating and domestic hot water. This again, is mainly because of one single assumption; the allocation of the emissions associated with waste incineration to the district heating production. In the present LCA, an emission intensity for heat production for waste incineration of 161.5 g CO2/kWh based on criteria from the ZEB Centre [53] was used. Figure 8 shows that if the emissions from waste incineration are not allocated to the heat production, the total emissions would decrease with as much as 25.2%. Hence, a change in this parameter will make considerable impact on the total results. Whether or not the assumption used here is right is debatable. On one hand it can be argued that heat is a by-product from the waste incineration process, and therefore should be allocated to the waste management system. This is currently the allocation principle that is suggested in the proposed new Norwegian standard prNS 3720.
On the other side, as pointed out by M. Lien [54]: “waste is today an internationally tradable commodity that should be utilized where it gives maximum energy per unit greenhouse gas emitted”. In such a view, emissions from waste incineration should clearly be allocated to
Something that may be surprising is that when the Norwegian emission intensity is used and with the assumption of symmetric weighting (the same emission intensity for import and export), the negative emissions “gained” from on-site production does not even cover the emissions embodied in the PV panels (see Figure 6). Here, and also for several of the other elements, the choice of emission intensity for electricity becomes relevant. Similar to the intensity for district heat, also this is a debated subject in LCA studies [55-57]. First of all, the future electricity mix is hard to predict. Further, the electricity network is a complex system with varying exchange of energy between countries and continents, depending on season, accessibility and propagation of transfer possibilities. The sensitivity ratio for the intensity indicates that a change in this parameter does not drastically affect the total result, see Table 3. This however, is when all the emissions are included, also the negative emissions associated with the on-site production of electricity from the PV panels. Because symmetric weighting is assumed, both the positive and negative emissions increase when changing the emission intensity. If the negative emissions are disregarded, the total emissions from the neighbourhood (including all elements) would increase with 30% when changing from
scenario 1 (NO) to scenario 2 (EU28+NO). This clearly shows how critical this parameter is for the results. Because of the high sensitivity of the emission intensity of electricity, it is
important to adopt a value (and evolution) that is as realistic as possible to facilitate decision making and choices of energy system in early stage planning.
The emissions from mobility constitute 40% of the total neighbourhood emissions and out of this 37% come from the operation of the transportation modes. If the system boundaries are adjusted to match the ones examined by Bastos et al. [35], large differences in the results are revealed. While Bastos et al. found that transportation contributed with 51-57% of the emission when buildings (materials and operation) and transportation of the users were included, the comparable percentage was only 22% in the present study. This is probably partly because of inclusion of (an optimistic?) future evolution of the personal vehicle stock regarding the share of electric vehicles, in combination with the low emission intensity for electricity. The remaining 63% of the emissions from mobility come from the production of vehicles. If adopting the system boundaries used by Anderson et al. [30] including buildings and mobility, the product stage for vehicles constitutes 27%, which is exactly the same as reported by Anderson et al. Their study however, concludes that emissions from the operation stage constitute a larger share than the vehicle production, something that may indicate that the agreeing percentages are a coincidence.
The open spaces element consisting of roads, sidewalks, outside parking, and public lighting together with the network element including the district heating pipes only constitute a total of 2.3% of the total neighbourhood emissions over the lifetime. It is expected that this number will be higher “as-built” due to possible underestimated amounts of materials included in the model, as well as lack of detailed data for the modules. The low share still indicates a relatively small contribution when comparing to the building and mobility elements.
Performing an LCA in early stage planning of projects is useful to gain knowledge that serves as basis for decision making. Some choices that are done in early stage are crucial for the
design of the project and will affect the environmental impacts in the entire lifetime.
Examples here are structural building materials, spatial planning and choice of energy system. Some choices are more difficult to control, e.g. the evolution of the energy mix in electricity and district heat and the evolution of vehicle stocks. However, it is possible to address these uncertainties by choosing a flexible energy system, such as waterborne heat systems in the buildings and by dimensioning the electricity network to be able to meet a growing electrical vehicle stock. In practice, when performing LCA at an early stage, the main focus should be on the decisions that facilitate as low as possible emissions in the future.
4.2. Limitations and Further Work
Although the model has several advantages in highlighting the dominant drivers both related to physical elements and life cycle stages and facilitating for comparability between design choices and between projects, there are still limitations that weaken the model.
First of all, the model does not account for long term changes in technology development and improvements in production processes for the replacement materials. The only
exception is for the PV panels, where the emissions are assumed to decrease with 50% in the replacement. This affects especially mobility emissions due to the frequent replacements of vehicles. With the current rapid technology improvement in the transportation sector, especially for electric vehicles, there will be less emissions from production processes, both for the vehicle itself and for their fuel cycles. Further research is required to make realistic and quantitative scenarios on production of vehicles in the future. Emissions per distance for 2010 as reported by Simonsen [47], and recommendations as in the proposed new standard prNS 3720 [38], are not sufficient to do robust calculations on neighbourhoods with an analysis period of 60 years.
Together with emissions associated with replacements of materials (and vehicles), there are also large uncertainties when it comes to the evolution of parameters as emission
intensities, the behaviour of inhabitants (travel habits, energy use etc.) and the distribution between vehicle types. In order to make the model more complete and realistic, more research is required on the likely future evolution.
When performing LCA, it is often considered several impact categories to show a holistic picture of the product or process. Here however, only climate change measured in greenhouse gas equivalent emissions is reported. A broader analysis is needed to avoid problem shifting phenomena, e.g. reduced GHG emissions but increased environmental impacts in other impact categories such as acidification, land use change and photochemical smog. Therefore, the LCA model should be extended to also consider other relevant impact categories.
At last, the model is based on yearly values rather than hourly data for consumption and production of energy. In practice this means that the external electricity network is considered an infinite capacity battery and that it does not make any difference if the self- produced electricity is consumed locally in the neighbourhood or exported to the grid. This
used and that the intensity is constant over the year. This is a simplification and may not reflect reality. Also, if the economic perspective is added, the prices of imported vs. exported energy is commonly asymmetric, which favours a high self-consumption, because the price of exported energy is usually less than the price for import. Here, also other factors as energy storage and vehicle-to-grid concepts become relevant, however, they are outside the scope of this study.
